Application of Artificial Neural Networks to Power System State Estimation

State estimation function is essential for effective and timely execution of power system automation and control systems, especially in modern active distribution systems where more intermittent renewable energy systems are integrated into the grid. Distribution system state estimation faces a lot of challenges including lack of monitoring devices and possible incorrect topology information. Developing efficient state estimation for distribution systems is thus of great interest. This paper presents results on utilizing artificial neural networks for this purpose. Artificial neural networks have been used in power distribution system state estimation. However, there is a lack of systematic analysis and study of which types of ANNs and what structures including parameters are most suitable for state estimation applications. When designing an ANN for a state estimator, trial and error approach has been common and there is no systematic method available to guide the process. The ultimate goal of the research is to examine the performance of various types of ANNs (e.g., Multi-Layer Perceptron (MLPs), Convolutional Neural Networks (CNNs) and Long-Short- Term-Memory Networks (LSTMs)) with different structures and also provide possible guidance on how to choose the different parameters, including model parameters such as number of hidden layers and number neurons in a layer, and algorithm parameters such as adjustable learning rate, for desired performance metrics. The paper presents preliminary results based on MLPs. IEEE standard 34-bus test system is used to illustrate the proposed methods and their effectiveness. The paper seeks to contribute to a more systematic approach to neural network and deep learning applied to power system state estimation, thus enhancing situational awareness, system resiliency and real-time monitoring and control of power distribution systems. Successful state estimation function will increase the ability of distribution systems to integrate more renewable energy based generations

State estimation of integrated power and gas distribution networks

Future energy networks are likely to be highly integrated with several energy conversion utilities operating between them, which make the control and management of the whole system more complicated. Therefore, analysis of operation and management of the whole system needs to be performed in an integrated approach. In order to perform an effective control and management of the whole system, an accurate and reliable estimation of the state parameters and the energy flows within the integrated network is essential. In this research simulation and state estimation of integrated power and gas distribution networks with decentralised injection and generation in both networks was investigated. For this purpose, state estimation of individual networks was first reviewed. Afterwards, state estimation of integrated power and gas distribution networks was studied. Firstly, an algorithm was developed for state estimation of power distribution networks, which was validated through a case study power distribution network. Afterwards, an algorithm for placement of additional measurements within power distribution networks for improvement of state estimation results was developed. The performance of the algorithm showed satisfactory results for placement of a given number of additional individual measurements and a given number of additional measurement units. Secondly, an algorithm was developed for simulation of operation of gas distribution networks with decentralised injection, which was validated with the results of the commercial software Synergi Gas. Then, an algorithm was developed for the WLS state estimation of gas distribution networks with decentralised injection, which was validated on a case study gas distribution network. Afterwards, an algorithm was developed for placement of additional measurements within gas distribution networks with decentralised injection for improvement of estimation of the gas mixtures within the network, which showed satisfactory results on a case study gas distribution network. Finally, an algorithm was developed for performing state estimation of power and gas distribution networks with decentralised injection and generation in both networks, which was validated on a case study power and gas distribution network. Afterwards, impact of deployment of smart meters on improvement of estimation of the state parameters of the other coupled network was investigated. It was observed that information from one of the energy networks has no significant impact on improvement of state estimation results of the other coupled networ

Ensemble estimation and analysis of network parameters: strengthening the GIC modelling chain

Ensemble Estimation and Analysis of Network Parameters - Strengthening the GIC Modelling Chain - Abstract Large grounded conducting networks on Earth's surface have long been known to be affected by solar activity and geomagnetic storms. Geomagnetically induced currents (GICs) in these quasiantennas are just one of the effects. In modern times, society has become more and more dependent on electrical power and, as a result, power networks. These power networks form extensive grounded conductors and are susceptible to GICs, even at mid-latitude regions. Given a large enough event now, such as the Carrington event of 1859, the direct and knock-on results can be devastating. Such an event is more than just a possibility, it is just a matter of time. With this in mind, the study of the effects of GICs and the modelling of them has become essential to ensure the future security of society in general. GIC modelling makes the assumption that the resultant GIC at a specific node in a power network is assumed to be linearly related to the horizontal vector components of the geoelectric field, which is induced by a plane-wave geomagnetic field. The linear GIC and geoelectric field relation is linked by a pair of network parameters, a and b. These parameters are not easily measurable explicitly but may be estimated empirically. Furthermore, these parameters are traditionally only seen to include network information and remain constant given a stable network. In this work, a new empirical approach to derive estimates for a and b is presented where the linear relation is solved simultaneously for all possible pair of time instances. Given a geomagnetic storm time-series (length n) of simultaneous GIC and geoelectric field data to solve for a and b, taking all possible time instance pairs yields approximately N²/2 estimates for a and b. The resulting ensembles of parameter estimates are analysed and found to be approximately Cauchy-distributed. Each individual estimate resulting from a single pair of time instances being solved is not the true state of the system, but a possible state. Taking the ensemble as a whole though gives the most probable parameter estimate, which in the case of a Cauchy distribution is the median. These ensemble parameter estimates are used in the engineering link of the modelling chain, but the ensembles themselves allow further analysis into the nature of GICs. An improvement is seen when comparing the performance of the ensemble estimates applied to an out-of-sample dataset during the Halloween Storm of 2003 with previous GIC modelling in the South African power network using the same dataset. Analysis of the ensembles has verified certain ground assumptions (specifically the plane-wave assumption and network directionality) made as a first-order approximation in GIC modelling and has also shown that errors from these assumptions are absorbed into empirically derived network parameters. Using a range of estimates from the ensemble, a GIC prediction band is produced. This in itself corresponds to an error estimate in the prediction. For the first time, it has been explicitly shown that empirically derived network parameters show a correlation to the magnitude of the produced GIC. This behaviour is then used to refine the parameter estimation further and allow for real time dynamic network parameter estimation that further improves modelling

Design and analysis of dynamic compressive sensing in distribution grids

Doctor of PhilosophyDepartment of Electrical and Computer EngineeringBalasubramaniam NatarajanThe transition to a smart distribution grid is powered by enhanced sensing and advanced metering infrastructure that can provide situational awareness. However, aggregating data from spatially dispersed sensors/smart meters can present a significant challenge. Additionally, the lack of reliability in communication network used for aggregating this data, prevents its use for real time operations such as state estimation and control. With these challenges associated with measurement availability and accessibility, current distribution systems are typically unobservable. To cope with the unobservability issue, compressive sensing (CS) theory allows us to recover system state information from a small number of measurements provided the states of the distribution system exhibit sparsity. The spatio-temporal correlation of loads and/or rooftop photovoltaic (PV) generation results in sparsity of distribution system states. In this dissertation, we first validate this system sparsity property and exploit it to develop two (direct/indirect) voltage state estimation strategies for a three-phase unbalanced distribution network. Secondly, we focus on addressing the challenge of sparse signal recovery from limited measurements while incorporating their temporal dependence. Specifically, we implement two recursive dynamic CS approaches namely, streaming modified weighted-L1 CS and Kalman filtered CS that reconstruct a sparse signal using the current underdetermined measurements and the prior information about the sparse signal and its support set. Using practical distribution system power measurements as a case study, we quantify, for the first time, the performance improvement achievable with such recursive techniques relative to batch algorithms. CS based signal recovery efforts typically assume that a limited number of measurements are available. However, in practice, due to communication network impairments, there is no guarantee that even this limited set of information might be available at the time of processing at the fusion/control center. Therefore, for the first time, we investigate the impact of intermittent measurement availability and random delays on recursive dynamic CS. Specifically, we quantify the error dynamics in both sparse signal estimation and support set estimation for a modified Kalman filter-CS based strategy in the presence of measurement losses. Using input-to-state stability analysis, we provide an upper bound for the expected covariance of the estimation error for a given rate of information loss. Next, we develop a modified CS algorithm that leverages apriori knowledge of signal correlation to project delayed measurements to the current signal recovery instant. We derive a new result quantifying the impact of errors in the apriori correlation model on signal recovery error. Lastly, we study the robustness of CS based state estimation to uncertainty in distribution network topology knowledge. Topology identification is a challenging problem in distribution systems in general and especially, when there are limited number of available measurements. We tackle this problem by jointly estimating the states and network topology via an integrated mixed integer nonlinear program formulation. By developing convex relaxations of the original formulation as well Markovian models for dynamic topology transitions, we illustrate the superior performance achieved in both state estimation and in topology identification. In summary, this dissertation offers the first comprehensive treatment of dynamic CS in smart distribution grids and can serve as the foundation of numerous follow-on efforts related to networked state estimation and control

Diagnostic Applications for Micro-Synchrophasor Measurements

This report articulates and justifies the preliminary selection of diagnostic applications for data from micro-synchrophasors (µPMUs) in electric power distribution systems that will be further studied and developed within the scope of the three-year ARPA-e award titled Micro-synchrophasors for Distribution Systems

Impact of New Madrid Seismic Zone Earthquakes on the Central USA, Vol. 1 and 2

The information presented in this report has been developed to support the Catastrophic Earthquake Planning Scenario workshops held by the Federal Emergency Management Agency. Four FEMA Regions (Regions IV, V, VI and VII) were involved in the New Madrid Seismic Zone (NMSZ) scenario workshops. The four FEMA Regions include eight states, namely Illinois, Indiana, Kentucky, Tennessee, Alabama, Mississippi, Arkansas and Missouri. The earthquake impact assessment presented hereafter employs an analysis methodology comprising three major components: hazard, inventory and fragility (or vulnerability). The hazard characterizes not only the shaking of the ground but also the consequential transient and permanent deformation of the ground due to strong ground shaking as well as fire and flooding. The inventory comprises all assets in a specific region, including the built environment and population data. Fragility or vulnerability functions relate the severity of shaking to the likelihood of reaching or exceeding damage states (light, moderate, extensive and near-collapse, for example). Social impact models are also included and employ physical infrastructure damage results to estimate the effects on exposed communities. Whereas the modeling software packages used (HAZUS MR3; FEMA, 2008; and MAEviz, Mid-America Earthquake Center, 2008) provide default values for all of the above, most of these default values were replaced by components of traceable provenance and higher reliability than the default data, as described below. The hazard employed in this investigation includes ground shaking for a single scenario event representing the rupture of all three New Madrid fault segments. The NMSZ consists of three fault segments: the northeast segment, the reelfoot thrust or central segment, and the southwest segment. Each segment is assumed to generate a deterministic magnitude 7.7 (Mw7.7) earthquake caused by a rupture over the entire length of the segment. US Geological Survey (USGS) approved the employed magnitude and hazard approach. The combined rupture of all three segments simultaneously is designed to approximate the sequential rupture of all three segments over time. The magnitude of Mw7.7 is retained for the combined rupture. Full liquefaction susceptibility maps for the entire region have been developed and are used in this study. Inventory is enhanced through the use of the Homeland Security Infrastructure Program (HSIP) 2007 and 2008 Gold Datasets (NGA Office of America, 2007). These datasets contain various types of critical infrastructure that are key inventory components for earthquake impact assessment. Transportation and utility facility inventories are improved while regional natural gas and oil pipelines are added to the inventory, alongside high potential loss facility inventories. The National Bridge Inventory (NBI, 2008) and other state and independent data sources are utilized to improve the inventory. New fragility functions derived by the MAE Center are employed in this study for both buildings and bridges providing more regionally-applicable estimations of damage for these infrastructure components. Default fragility values are used to determine damage likelihoods for all other infrastructure components. The study reports new analysis using MAE Center-developed transportation network flow models that estimate changes in traffic flow and travel time due to earthquake damage. Utility network modeling was also undertaken to provide damage estimates for facilities and pipelines. An approximate flood risk model was assembled to identify areas that are likely to be flooded as a result of dam or levee failure. Social vulnerability identifies portions of the eight-state study region that are especially vulnerable due to various factors such as age, income, disability, and language proficiency. Social impact models include estimates of displaced and shelter-seeking populations as well as commodities and medical requirements. Lastly, search and rescue requirements quantify the number of teams and personnel required to clear debris and search for trapped victims. The results indicate that Tennessee, Arkansas, and Missouri are most severely impacted. Illinois and Kentucky are also impacted, though not as severely as the previous three states. Nearly 715,000 buildings are damaged in the eight-state study region. About 42,000 search and rescue personnel working in 1,500 teams are required to respond to the earthquakes. Damage to critical infrastructure (essential facilities, transportation and utility lifelines) is substantial in the 140 impacted counties near the rupture zone, including 3,500 damaged bridges and nearly 425,000 breaks and leaks to both local and interstate pipelines. Approximately 2.6 million households are without power after the earthquake. Nearly 86,000 injuries and fatalities result from damage to infrastructure. Nearly 130 hospitals are damaged and most are located in the impacted counties near the rupture zone. There is extensive damage and substantial travel delays in both Memphis, Tennessee, and St. Louis, Missouri, thus hampering search and rescue as well as evacuation. Moreover roughly 15 major bridges are unusable. Three days after the earthquake, 7.2 million people are still displaced and 2 million people seek temporary shelter. Direct economic losses for the eight states total nearly $300 billion, while indirect losses may be at least twice this amount. The contents of this report provide the various assumptions used to arrive at the impact estimates, detailed background on the above quantitative consequences, and a breakdown of the figures per sector at the FEMA region and state levels. The information is presented in a manner suitable for personnel and agencies responsible for establishing response plans based on likely impacts of plausible earthquakes in the central USA.Armu W0132T-06-02unpublishednot peer reviewe